Comparaison of VP and DP derived neurons differentiation trajectories

Load libraries and QCFiltered dataset

# Load library
library(Seurat)
library(princurve)
library(monocle)
library(FateID)
library(velocyto.R)
library(parallel)
library(seriation)
library(dplyr)
library(reshape2)
library(ggplot2)
library(cowplot)
library(ggExtra)
library(patchwork)
library(RColorBrewer)
library(wesanderson)

#Set ggplot theme as classic
theme_set(theme_classic())

# Load the full annotated dataset
Allcells.data <- readRDS("./Clustered.cells.RDS")

colors <-  c("#969696",
              tolower(c("#68B041", "#E3C148", "#B7D174", "#83C3B8", "#009FDA", "#3E69AC", "#E46B6B")),
              "#ec756d", "#c773a7", "#7293c8", "#b79f0b", "#3ca73f","#31b6bd",
              "#ebcb2e", "#9ec22f", "#a9961b", "#cc3a1b", "#cc8778" , "#d14c8d", "#4cabdc", "#5ab793", "#e7823a","#e6bb9b", "#046c9a", "#4784a2" , "#4990c9")

DimPlot(Allcells.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 2,
        no.legend = F,
        cols.use = colors)

Pallial transition state cells

Extract apical progenitors

We perform Kmeans clustering on the 4 cell state scores :

• Sub-pallial neurons SP
• Pallial neurons Pal
• Sub-pallial BP SP.BP
• Pallial BP Pal.BP

#Calculate Pallial and Sub-pallial BP scores
Pal.BP.genes <- c("Eomes", "Neurog2", "Neurog1", "Prmt8", "Nrp1")
genes.list <- list(Pal.BP.genes)
enrich.name <- "Pal.BP_signature"
Allcells.data <- AddModuleScore(Allcells.data, genes.list = genes.list, genes.pool = NULL, n.bin = 5,
                          seed.use = 1, ctrl.size = length(genes.list), use.k = FALSE, enrich.name = enrich.name ,
                          random.seed = 1)


SP.BP.genes <- c("Dlx1", "Dlx2", "Dlx5","Ascl1", "Gsx2")
genes.list <- list(SP.BP.genes)
enrich.name <- "SP.BP_signature"
Allcells.data <- AddModuleScore(Allcells.data, genes.list = genes.list, genes.pool = NULL, n.bin = 5,
                          seed.use = 1, ctrl.size = length(genes.list), use.k = FALSE, enrich.name = enrich.name ,
                          random.seed = 1)

set.seed(100)
# Run Kmeans clustering
cl <- kmeans(cbind(Allcells.data@meta.data$SP_signature1,
                   Allcells.data@meta.data$Pal_signature1,
                   Allcells.data@meta.data$SP.BP_signature1,
                   Allcells.data@meta.data$Pal.BP_signature1), 4)

Allcells.data@meta.data$kmeanClust <- paste0("Clust.",cl$cluster)

col.pal <- wes_palette("GrandBudapest1", 4, type = "discrete")

p1 <- ggplot(Allcells.data@meta.data, aes(x=SP_signature1, y=Pal_signature1, colour = kmeanClust)) +
  scale_color_manual(values=col.pal) +
  geom_point() + 
  theme(legend.position="none")
ggMarginal(p1, type = "histogram", fill="lightgrey")

DimPlot(Allcells.data,
        group.by = "kmeanClust",
        reduction.use = "spring",
        cols.use = col.pal,
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = F)

We then extract the Pallial cells branche. We also excludes CR cells cluster form the trajectory inference.

#Remove the sub-pallial cells branche
MeanKclust.SPscore <- aggregate(SP_signature1 ~ kmeanClust, Allcells.data@meta.data, mean)
SPclust <- MeanKclust.SPscore %>% filter(SP_signature1 == max(SP_signature1)) %>% pull(kmeanClust)

SP.cells <- Allcells.data@meta.data %>% filter(kmeanClust == SPclust) %>% pull(Barcodes)

# Remove cells not use for trajectory inference (Subpallial cells defined by kmeans, Late GABA neurons and CR cells)
Excluded.clusters <- Allcells.data@meta.data %>%
                  filter(Cluster.ident %in% grep("*Sub|GABA|LN.Glut.13|LN.Glut.14|LN.Glut.1$", unique(as.character(Allcells.data@ident)), value = T)) %>%
                  pull(Barcodes) 

# We further keep only pallial apical progenitor cluster among other AP clusters
MeanKclust.APscore <- aggregate(AP_signature1 ~ kmeanClust, Allcells.data@meta.data, mean)
APclust <- MeanKclust.APscore %>% filter(AP_signature1 == max(AP_signature1)) %>% pull(kmeanClust)

All.AP <- Allcells.data@meta.data %>% filter(kmeanClust == APclust) %>% pull(Barcodes)
Valide.AP <- Allcells.data@meta.data %>% filter(Cluster.ident %in% grep("Dorsal.Pallium|lateral.Pallium.1|lateral.Pallium.2|Ventral.Pallium",
                                                unique(as.character(Allcells.data@ident)), value = T)) %>% pull(Barcodes)

filtered.AP <- All.AP[!All.AP %in% Valide.AP]

#Remove all invalide cells + 3 pallial outlier cells
Cells.to.use <- rownames(Allcells.data@meta.data)[!rownames(Allcells.data@meta.data) %in% unique(c(SP.cells, Excluded.clusters, filtered.AP, c("ATTTCTGCACGGCCAT" , "CAAGTTGCAAGCCTAT", "CTACATTGTAGCTGCC")))]
Allcells.data <- SubsetData(Allcells.data, cells.use = Cells.to.use, subset.raw = T,  do.clean = F)

colors <-  c("#969696",
              tolower(c("#68B041", "#E3C148", "#B7D174", "#E46B6B")),
              "#cc3a1b", "#cc8778" , "#d14c8d", "#4cabdc", "#5ab793", "#e7823a","#e6bb9b", "#046c9a", "#4784a2" , "#4990c9")

DimPlot(Allcells.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 2,
        no.legend = T,
        cols.use = colors)

Filter the gene counts matrix

# Filter genes
num.cells <- Matrix::rowSums(Allcells.data@raw.data > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 20)])
Allcells.data@raw.data <- Allcells.data@raw.data[genes.use, ]

# Normalization and variable gene selection
Allcells.data <- NormalizeData(object = Allcells.data,
                         normalization.method = "LogNormalize", 
                         scale.factor = round(median(Allcells.data@meta.data$nUMI)),
                         display.progress = F)

Allcells.data <- FindVariableGenes(object = Allcells.data,
                             mean.function = ExpMean,
                             dispersion.function = LogVMR,
                             x.low.cutoff = 0.03,
                             x.high.cutoff = 4,
                             y.cutoff = 2, do.plot = F,
                             display.progress = F)

Velocity estimation

Set Cluster Id and color palette for velocity internal plotting functions

# Take the cluster id from Seurat analysis
cluster.label <- Allcells.data@ident
Cluster.ident <- as.character(Allcells.data@ident)

#Set color Palette for layout
palette <- c(scales::hue_pal()(length(unique(Cluster.ident))))
colorsident <- cbind(ident = sort(unique(Cluster.ident)),
                     colors = c("#969696",
                                tolower(c("#68B041", "#E3C148", "#B7D174", "#E46B6B")),
                                "#cc3a1b", "#cc8778" , "#d14c8d", "#4cabdc", "#5ab793", "#e7823a","#e6bb9b", "#046c9a", "#4784a2" , "#4990c9") )

#Create annotation data.frame
Cells.Color.df <- data.frame(sample_name = row.names(Allcells.data@meta.data),
                                primary_type_label = as.character(Cluster.ident),
                                primary_type_color = as.character(colorsident[match(Cluster.ident, colorsident[,1]),2]))


cell.colors <- Cells.Color.df$primary_type_color
names(cell.colors) <- Cells.Color.df$sample_name

rm(Cells.Color.df,colorsident,palette)

Load velocito Loom file containing spliced / unspliced gene count matrix

#Load Loom file containing Spliced VS Unspliced transcripts count matrices
LoomPath <- "./velocyto/E12-WT.loom" 
ldat <- read.loom.matrices(LoomPath)

## reading loom file via hdf5r...

BarcodesVelocity <- stringi::stri_sub(ldat$spliced@Dimnames[[2]],40,55)

ldat$spliced@Dimnames[[2]] <- BarcodesVelocity
ldat$unspliced@Dimnames[[2]] <- BarcodesVelocity
ldat$ambiguous@Dimnames[[2]] <- BarcodesVelocity

#Filter matrix by barcodes present in Seurat object (cell to retain for the analysis)
ldat$spliced <- ldat$spliced[,rownames(Allcells.data@meta.data)]
ldat$unspliced <- ldat$unspliced[,rownames(Allcells.data@meta.data)]

#Remove genes with duplicated rows
ldat$spliced <- ldat$spliced[!duplicated(ldat[["unspliced"]]@Dimnames[[1]]),]
ldat$unspliced <- ldat$unspliced[!duplicated(ldat[["unspliced"]]@Dimnames[[1]]),]

#Clean workspace
rm(list=ls()[!ls() %in% c("Allcells.data", "ldat", "cell.colors", "cluster.label")])

Select genes to use for velocity estimation and set cells’ association metric

spliced <- ldat$spliced
unspliced <- ldat$unspliced

#Filter genes by cluster expression
Filtered.spliced <- filter.genes.by.cluster.expression(spliced,cluster.label,min.max.cluster.average = 0.3)
Filtered.unspliced <- filter.genes.by.cluster.expression(unspliced,cluster.label,min.max.cluster.average = 0.3)

# Filter Cell cycle associated genes to stress only the neurogenic composante
CCgenes <- as.character(read.table("./Progenitors/CellCycleGenes.csv", sep = "\t", header = F)[,1])

Filtered.spliced  <- Filtered.spliced[!rownames(Filtered.spliced) %in% CCgenes,]
Filtered.unspliced <- Filtered.unspliced[!rownames(Filtered.unspliced) %in% CCgenes,]

Allcells.data <- RunPCA(object = Allcells.data,
                        pcs.compute = 20,
                        do.print = F)

#Select association metric use for velocity estimation
cell.dist <- as.dist(1-armaCor(t(Allcells.data@dr$pca@cell.embeddings)))

Velocity estimation and visualisation

#Velocity estimation
fit.quantile <- 0.04
rvel.cd <- gene.relative.velocity.estimates(Filtered.spliced,
                                            Filtered.unspliced,
                                            deltaT=1,
                                            kCells = 8,
                                            kGenes = 10,
                                            cell.dist=cell.dist,
                                            fit.quantile=fit.quantile,
                                            n.cores = detectCores() -2)

## calculating cell knn ... done
## calculating convolved matrices ... done
## gene kNN ... scaling gene weights ... convolving matrices ... done
## fitting gamma coefficients ... done. succesfful fit for 2423 genes
## filtered out 3 out of 2423 genes due to low nmat-emat correlation
## filtered out 10 out of 2420 genes due to low nmat-emat slope
## calculating RNA velocity shift ... re-estimating gamma of individual genes ... done
## calculating RNA velocity shift ... done
## calculating extrapolated cell state ... done

#Velocity on embedding
emb <- Allcells.data@dr$spring@cell.embeddings/50

Spring.velo <- show.velocity.on.embedding.cor(emb,
                                              rvel.cd,
                                              n=50,
                                              scale='sqrt',
                                              cex=0.8,
                                              arrow.scale=0.7,
                                              show.grid.flow=T,
                                              min.grid.cell.mass=0.5,
                                              grid.n=40,
                                              arrow.lwd=1,
                                              do.par=F,
                                              cell.border.alpha = 0,
                                              cell.colors=cell.colors,
                                              expression.scaling = T,
                                              return.details= T,
                                              n.core= detectCores() -2)

## delta projections ... sqrt knn ... transition probs ... done
## calculating arrows ... done
## grid estimates ... grid.sd= 0.288576  min.arrow.size= 0.005771519  max.grid.arrow.length= 0.04106169  done
## expression shifts .... done

Spring.plot <- as.data.frame(Allcells.data@dr$spring@cell.embeddings/50) %>%
               mutate(x0 = Spring.velo$arrows[, "x0"],
                      x1 = Spring.velo$arrows[, "x1"],
                      y0 = Spring.velo$arrows[, "y0"],
                      y1 = Spring.velo$arrows[, "y1"]) %>%
               mutate(x2 = x0 + (x1 - x0),
                      y2 = y0 + (y1 - y0)) %>%
               mutate(Cluster = Allcells.data@meta.data$Cluster.ident)

colors <- c("#969696",
            tolower(c("#68B041", "#E3C148", "#B7D174", "#E46B6B")),
            "#cc3a1b", "#cc8778" , "#d14c8d", "#4cabdc", "#5ab793", "#e7823a","#e6bb9b", "#046c9a", "#4784a2" , "#4990c9")

ggplot(Spring.plot) +
    geom_point(aes(x = spring1, y = spring2, colour = Cluster)) +
    scale_color_manual(values = colors) +
    geom_segment(aes(x = x0, xend = x2, y = y0, yend = y2),
                 arrow = arrow(length = unit(3, "points"), type = "closed"),
                 colour = "grey20", alpha = 0.8) + 
   theme(legend.position="none")

global.vel.arrow <- Spring.velo$garrows %>%
                    as.data.frame() %>%
                     mutate(x2 = x0 + (x1 - x0),
                            y2 = y0 + (y1 - y0))

ggplot(Spring.plot) +
    geom_point(aes(x = spring1, y = spring2, colour = Cluster)) +
    scale_color_manual(values = colors) +
    geom_segment(data = global.vel.arrow,
                 aes(x = x0, xend = x2, y = y0, yend = y2),
                 size = 1,
                 arrow = arrow(length = unit(3, "points"), type = "open"),
                 colour = "grey20", alpha = 0.8) + 
   theme(legend.position="none")

rm(list = ls()[!ls() %in% "Allcells.data"])

Compute cells’ fate bias along tpward each mature neuronal types

Prepare FateID input data

# FataID requieres the terminal clusters ID to be integers
TerminalFates <- grep("16|24|22|19|26|20|21", unique(as.character(Allcells.data@ident)), value = T)

Allcells.data@meta.data$NewClusterID <- sapply(as.character(Allcells.data@ident),
                                         function(x) if(x == TerminalFates[1]){x=1}
                                         else if(x== TerminalFates[2]){x=2}
                                         else if(x== TerminalFates[3]){x=3}
                                         else if(x== TerminalFates[4]){x=4}
                                         else if(x== TerminalFates[5]){x=5}
                                         else if(x== TerminalFates[6]){x=6}
                                         else if(x== TerminalFates[7]){x=7}
                                         else{x=x})
Allcells.data <- SetAllIdent(Allcells.data, id = "NewClusterID")

colors <-  c("#cc8778" , "#d14c8d", "#4cabdc", "#5ab793", "#e7823a","#e6bb9b","#cc3a1b",
             "#969696",
              tolower(c("#68B041", "#E3C148", "#B7D174", "#E46B6B")),
              "#046c9a", "#4784a2" , "#4990c9")

DimPlot(Allcells.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = F,
        cols.use = colors)

We restricted the analysis to the most variable genes as dertermined by the Seurat function “FindVariableGenes” excluding cell cylce phase genes

# Exclude cell cycle associated genes
CCgenes <- as.character(read.table("./Progenitors/CellCycleGenes.csv", sep = "\t", header = F)[,1])
Input.genes <- Allcells.data@var.genes[!Allcells.data@var.genes %in% CCgenes]

# FateID requieres the expression matrix to be a dataframe
Norm.Mat <- as.data.frame(as.matrix(Allcells.data@data[Input.genes,]))

# Set a cluster assignment factor for each cells
ClusterIdent <- as.character(Allcells.data@ident)
names(ClusterIdent) <- names(Allcells.data@ident)

# Finaly FateID requires to specify the identity of the attractor state clusters
Attractors <- 1:7

Compte the cells’ fate bias

Infered.Fate.bias  <- fateBias(Norm.Mat, ClusterIdent, Attractors,
                               z = 1-cor(Norm.Mat, method = "spearman"),
                               minnr=10,
                               minnrh=30,
                               adapt=TRUE,
                               confidence=0.95,
                               nbfactor=5,
                               use.dist=FALSE,
                               seed=1234,
                               nbtree=NULL)

## minnr: 10 
## minnrh: 30 
## test set size iteration 1 : 10 10 10 10 10 10 10 
## randomforest iteration 1 of 50 cells
## test set size iteration 2 : 10 10 10 10 10 10 10 
## randomforest iteration 2 of 62 cells
## test set size iteration 3 : 10 10 10 10 10 10 10 
## randomforest iteration 3 of 53 cells
## test set size iteration 4 : 10 10 10 10 10 10 10 
## randomforest iteration 4 of 55 cells
## test set size iteration 5 : 10 10 10 10 10 10 10 
## randomforest iteration 5 of 49 cells
## test set size iteration 6 : 10 10 10 10 10 10 10 
## randomforest iteration 6 of 48 cells
## test set size iteration 7 : 10 10 10 10 10 10 10 
## randomforest iteration 7 of 54 cells
## test set size iteration 8 : 10 10 10 10 10 10 10 
## randomforest iteration 8 of 49 cells
## test set size iteration 9 : 10 10 10 10 10 10 10 
## randomforest iteration 9 of 50 cells
## test set size iteration 10 : 10 10 10 10 10 10 10 
## randomforest iteration 10 of 51 cells
## test set size iteration 11 : 10 10 10 10 10 10 10 
## randomforest iteration 11 of 52 cells
## test set size iteration 12 : 10 10 10 10 10 10 10 
## randomforest iteration 12 of 44 cells
## test set size iteration 13 : 10 10 10 10 10 10 10 
## randomforest iteration 13 of 45 cells
## test set size iteration 14 : 10 10 10 10 10 10 10 
## randomforest iteration 14 of 42 cells
## test set size iteration 15 : 10 10 10 10 10 10 10 
## randomforest iteration 15 of 43 cells
## test set size iteration 16 : 10 10 10 10 10 10 10 
## randomforest iteration 16 of 40 cells
## test set size iteration 17 : 10 10 10 10 10 10 10 
## randomforest iteration 17 of 40 cells
## test set size iteration 18 : 10 10 10 10 10 10 10 
## randomforest iteration 18 of 37 cells
## test set size iteration 19 : 10 10 10 10 10 10 10 
## randomforest iteration 19 of 42 cells
## test set size iteration 20 : 10 10 10 10 10 10 10 
## randomforest iteration 20 of 43 cells
## test set size iteration 21 : 10 10 10 10 10 10 10 
## randomforest iteration 21 of 48 cells
## test set size iteration 22 : 10 10 10 10 10 10 10 
## randomforest iteration 22 of 47 cells
## test set size iteration 23 : 10 10 10 10 10 10 10 
## randomforest iteration 23 of 50 cells
## test set size iteration 24 : 10 10 10 10 10 10 10 
## randomforest iteration 24 of 52 cells
## test set size iteration 25 : 10 10 10 10 10 10 10 
## randomforest iteration 25 of 53 cells
## test set size iteration 26 : 10 10 10 10 10 10 10 
## randomforest iteration 26 of 57 cells
## test set size iteration 27 : 10 10 10 10 10 10 10 
## randomforest iteration 27 of 54 cells
## test set size iteration 28 : 10 10 10 10 10 10 10 
## randomforest iteration 28 of 49 cells
## test set size iteration 29 : 10 10 10 10 10 10 10 
## randomforest iteration 29 of 54 cells
## test set size iteration 30 : 10 10 10 10 10 10 10 
## randomforest iteration 30 of 51 cells
## test set size iteration 31 : 10 10 10 10 10 10 10 
## randomforest iteration 31 of 50 cells
## test set size iteration 32 : 10 10 10 10 10 10 10 
## randomforest iteration 32 of 55 cells
## test set size iteration 33 : 5 5 5 5 5 5 10 
## randomforest iteration 33 of 36 cells
## test set size iteration 34 : 10 10 10 10 10 10 10 
## randomforest iteration 34 of 56 cells
## test set size iteration 35 : 10 10 10 10 10 10 10 
## randomforest iteration 35 of 55 cells
## test set size iteration 36 : 10 10 10 10 10 10 10 
## randomforest iteration 36 of 55 cells
## test set size iteration 37 : 10 10 10 10 10 10 10 
## randomforest iteration 37 of 54 cells
## test set size iteration 38 : 10 10 10 10 10 10 10 
## randomforest iteration 38 of 56 cells
## test set size iteration 39 : 10 10 10 10 10 10 10 
## randomforest iteration 39 of 58 cells
## test set size iteration 40 : 10 10 10 10 10 10 10 
## randomforest iteration 40 of 47 cells
## test set size iteration 41 : 10 10 10 10 10 10 10 
## randomforest iteration 41 of 44 cells
## test set size iteration 42 : 10 10 10 10 10 10 10 
## randomforest iteration 42 of 30 cells
## test set size iteration 43 : 10 10 10 10 10 10 10 
## randomforest iteration 43 of 18 cells
## test set size iteration 44 : 10 10 10 10 10 10 10 
## randomforest iteration 44 of 2 cells

Inspect the test set use at classification iteration

Allcells.data@meta.data$FateID.iteration <- "0"
Allcells.data <- SetAllIdent(Allcells.data, id = "FateID.iteration")

for (i in seq(0, length(Infered.Fate.bias$rfl), by = 5)[-1]) {
  iter <- seq(i-4,i)
  Barcodes <- c()
  for (j in iter) {
    Barcodes <- c(Barcodes, names(Infered.Fate.bias$rfl[[j]]$test$predicted))
  }
  Allcells.data <- SetIdent(Allcells.data, cells.use = Barcodes, ident.use = paste0("iter ",iter[1]," to ", iter[4]))
}

DimPlot(Allcells.data,
        reduction.use = "spring",
        dim.1 = 1,dim.2 = 2,
        do.label=T,
        label.size = 3,
        no.legend = F,
        cols.use = c("#dfdfdf", colors))

Allcells.data <- SetAllIdent(Allcells.data, id = "Cluster.ident")

Import lineage bias into Seurat meta.data

probs <- Infered.Fate.bias$probs[,seq(length(Attractors))]

Allcells.data@meta.data$prob.Nr4a2 <- probs$t1
Allcells.data@meta.data$prob.Foxp2c <- probs$t2
Allcells.data@meta.data$prob.Ppp1r14c <- probs$t3
Allcells.data@meta.data$prob.Fezf1 <- probs$t4
Allcells.data@meta.data$prob.Foxp2a <- probs$t5
Allcells.data@meta.data$prob.Foxp2b <- probs$t6
Allcells.data@meta.data$prob.Nfix <- probs$t7

FeaturePlot(object = Allcells.data,
            features.plot = c("prob.Nr4a2", "prob.Nfix",
                              "prob.Ppp1r14c","prob.Fezf1",
                              "prob.Foxp2a", "prob.Foxp2b", "prob.Foxp2c"),
            cols.use = rev(RColorBrewer::brewer.pal(n = 11, name = "Spectral")),
            reduction.use = "spring",
            no.legend = T)

Manuscript Fig. 7A and S7A

New.data <- data.frame(barcode=Allcells.data@meta.data$Barcodes,
                       cluster= Allcells.data@meta.data$Cluster.ident,
                       spring1= Allcells.data@dr$spring@cell.embeddings[,1],
                       spring2= Allcells.data@dr$spring@cell.embeddings[,2],
                       Nr4a2.biased= Allcells.data@meta.data$prob.Nr4a2,
                       Nfix.biased= Allcells.data@meta.data$prob.Nfix,
                       Ppp1r14c.biased = Allcells.data@meta.data$prob.Ppp1r14c,
                       Fezf1.biased = Allcells.data@meta.data$prob.Fezf1)

New.data <- New.data %>% filter(!cluster %in% grep("26|20|21", unique(as.character(Allcells.data@ident)), value = T))

New.data$lineage.bias <- colnames(New.data[,5:8])[apply(New.data[,5:8],1,which.max)]

ggplot(New.data, aes(spring1, spring2, colour = lineage.bias)) +
  scale_color_manual(values=c("#e7823a","#cc391b","#026c9a","#d14c8d")) +
  geom_point() 

Spring projection where cells are grouped according to the lineage for which they received the maximum nb of vote

Extract the Nr4a2 and Nfix “lineage” cells

Find cells with strong biase toward Nr4a2 neurons

Allcells.data@meta.data$Nr4a2.biase <- ifelse(Allcells.data@meta.data$prob.Nr4a2 > 0.5 & abs(Allcells.data@meta.data$prob.Nfix- Allcells.data@meta.data$prob.Nr4a2) > 0.25, "Nr4a2.lineage", "Other.lineages" )

table(Allcells.data@meta.data$Nr4a2.biase)

## 
##  Nr4a2.lineage Other.lineages 
##            617           1682

Find cells with strong biase toward Nfix neurons

Allcells.data@meta.data$Nfix.biase <- ifelse(Allcells.data@meta.data$prob.Nfix > 0.5 & abs(Allcells.data@meta.data$prob.Nfix- Allcells.data@meta.data$prob.Nr4a2) > 0.25, "Nfix.lineage", "Other.lineages" )

table(Allcells.data@meta.data$Nfix.biase)

## 
##   Nfix.lineage Other.lineages 
##            520           1779

Plot all biased cells on spring embedding

Nr4a2 <- rownames(subset(Allcells.data@meta.data, Allcells.data@meta.data$Nr4a2.biase == "Nr4a2.lineage"))
Nfix <- rownames(subset(Allcells.data@meta.data, Allcells.data@meta.data$Nfix.biase == "Nfix.lineage"))

Allcells.data@meta.data$lineage.bias <- sapply(Allcells.data@meta.data$Barcodes,
                                               function(x) if(x %in% Nr4a2){x="Nr4a2.lineage"}
                                               else if(x %in% Nfix ){x= "Nfix.lineage"}
                                               else{x= "Not.assigned"})

DimPlot(Allcells.data,
        group.by = "lineage.bias",
        reduction.use = "spring",
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        cols.use = c("#cc391b","#969696","#026c9a"),
        no.legend = F)

Inspect fate bias among APs

# Fate biase in progenitor domaines
data <- melt(data.frame(Clusters = Allcells.data@meta.data$Cluster.ident,
                        Nfix.score = Allcells.data@meta.data$prob.Nfix,
                        Nr4a2.score = Allcells.data@meta.data$prob.Nr4a2))

colnames(data) <- c("Clusters", "lineage", "Score")

data %>%
     filter(Clusters %in% c("AP.Ventral.Pallium", "AP.lateral.Pallium.1", "AP.lateral.Pallium.2", "AP.Dorsal.Pallium")) %>%
     mutate(Clusters = factor(Clusters, levels =c("AP.Ventral.Pallium", "AP.lateral.Pallium.1", "AP.lateral.Pallium.2", "AP.Dorsal.Pallium"))) %>%
     ggplot(aes(x=factor(lineage, levels = c("Nr4a2.score", "Nfix.score") ), y=Score, fill= Clusters)) + 
        geom_boxplot(notch=TRUE) +
        geom_point(position=position_jitterdodge(jitter.width = 0.2), size = .5, aes(group=Clusters)) +
        scale_fill_manual(values= c("#68b041", "#e3c148", "#b7d174", "#e46b6b"))  +
        xlab("")

Extract Nr4a2 and Nfix biased cells

#Subset dataset to keep only cells which showed sufficient difference of fate probability
VP.DP.lineage.cells <- c(rownames(subset(Allcells.data@meta.data, Allcells.data@meta.data$Nr4a2.biase == "Nr4a2.lineage")),
                         rownames(subset(Allcells.data@meta.data, Allcells.data@meta.data$Nfix.biase == "Nfix.lineage")))


# We further only retain VP and DP  progenitor clusters
Allcells.data <- SubsetData(Allcells.data,
                            cells.use = VP.DP.lineage.cells,
                            ident.remove = c("AP.lateral.Pallium.1", "AP.lateral.Pallium.2") ,
                            subset.raw = T,
                            do.clean = F)


DimPlot(Allcells.data,
        reduction.use = "spring",
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        cols.use = c( "#dfdfdf","#68b041", "#e46b6b","#cc3a1b","#cc8778","#e6bb9b" ,"#046c9a","#4784a2"),
        no.legend = T)

Allcells.data@meta.data$Lineage <- ifelse(Allcells.data@meta.data$Nr4a2.biase == "Nr4a2.lineage", "Nr4a2.lineage", "Nfix.lineage")

DimPlot(Allcells.data,
        group.by = "Lineage",
        reduction.use = "spring",
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        cols.use = c("#cc391b","#026c9a"),
        label.size = 4,
        no.legend = F)

Manuscript Fig. 7B

rm(list = ls()[!ls() %in% c("Allcells.data")])

Aligne cells along Pseudotime

We decided to use spring space dimensionality reduction to fit the principale curve because it has been calculated on all cells together. Thus reflecting pan neuronal differentiation axis of varation.

Nr4a2 neurons Pseudotime ordering

Nr4a2.lineage.cells <- Allcells.data@meta.data %>%
                       filter(Lineage == "Nr4a2.lineage") %>%
                       pull(Barcodes)

## Fit principale curve on the data in the spring space
data.Nr4a2 <- as.data.frame(Allcells.data@dr$spring@cell.embeddings[Nr4a2.lineage.cells,1:2])

fit <- principal_curve(as.matrix(data.Nr4a2[,1:2]),
                       smoother='smooth_spline',
                       trace=TRUE,
                       stretch=2)

## Starting curve---distance^2: 4868119869
## Iteration 1---distance^2: 734019.9
## Iteration 2---distance^2: 770558.7
## Iteration 3---distance^2: 787358.8
## Iteration 4---distance^2: 794176.1
## Iteration 5---distance^2: 797375.2
## Iteration 6---distance^2: 799004.2
## Iteration 7---distance^2: 800140.8
## Iteration 8---distance^2: 800778.4

Nr4a2.pc.line <- as.data.frame(fit$s[order(fit$lambda),]) #The principal curve smoothed
data.Nr4a2$PseudotimeScore <- fit$lambda/max(fit$lambda)

data.Nr4a2$Cluster <- Allcells.data@meta.data %>%
                       filter(Lineage == "Nr4a2.lineage") %>%
                       pull(NewClusterID)

# Direction of the maturation score using Nes expression (reverte if positive correlation)
if (cor(data.Nr4a2$PseudotimeScore, Allcells.data@data['Hmga2', Nr4a2.lineage.cells]) > 0) {
  data.Nr4a2$PseudotimeScore <- -(data.Nr4a2$PseudotimeScore - max(data.Nr4a2$PseudotimeScore))
}

Nfix neurons Pseudotime ordering

Nfix.lineage.cells <- Allcells.data@meta.data %>%
                      filter(Lineage == "Nfix.lineage") %>%
                      pull(Barcodes)

## Fit principale curve on the data in the spring space
data.Nfix <- as.data.frame(Allcells.data@dr$spring@cell.embeddings[Nfix.lineage.cells,1:2])

fit <- principal_curve(as.matrix(data.Nfix[,1:2]),
                       smoother='smooth_spline',
                       trace=TRUE,
                       stretch=2)

## Starting curve---distance^2: 741666427
## Iteration 1---distance^2: 263692.7
## Iteration 2---distance^2: 257894.2
## Iteration 3---distance^2: 257508.9
## Iteration 4---distance^2: 257452.1

Nfix.pc.line <- as.data.frame(fit$s[order(fit$lambda),]) #The principal curve smoothed
data.Nfix$PseudotimeScore <- fit$lambda/max(fit$lambda)

data.Nfix$Cluster <- Allcells.data@meta.data %>%
                     filter(Lineage == "Nfix.lineage") %>%
                     pull(NewClusterID)


# Direction of the maturation score using Nes expression (reverte if positive correlation)
if (cor(data.Nfix$PseudotimeScore, Allcells.data@data['Hmga2', Nfix.lineage.cells]) > 0) {
  data.Nfix$PseudotimeScore <- -(data.Nfix$PseudotimeScore - max(data.Nfix$PseudotimeScore))
}

Combine both trajectory ordering

Pseudotime <- rbind(data.Nr4a2 %>% dplyr::select(PseudotimeScore),
                    data.Nfix %>% dplyr::select(PseudotimeScore))

Allcells.data@meta.data$Pseudotime <- Pseudotime[rownames(Allcells.data@meta.data),]

#Plot Pseudotime score with the 2 principal curves
data <- as.data.frame(Allcells.data@dr$spring@cell.embeddings[,1:2])
data$PseudotimeScore <- Allcells.data@meta.data$Pseudotime

cols <- colorRampPalette(brewer.pal(n =11, name = "Spectral"))(100)

ggplot(data, aes(spring1, spring2)) +
  geom_point(aes(color=PseudotimeScore), size=2, shape=16) + 
  scale_color_gradientn(colours=rev(cols), name='Speudotime score') +
  geom_line(data=Nfix.pc.line, color="#cc391b", size=0.77) +
  geom_line(data=Nr4a2.pc.line, color="#026c9a", size=0.77)

rm(list = ls()[!ls() %in% c("Allcells.data")])

Find DEG between the two trajectories and along the pseudotime

Filter gene counts matrix

#Filter genes
num.cells <- Matrix::rowSums(Allcells.data@raw.data > 0)
genes.use <- names(x = num.cells[num.cells >= 10])
Allcells.data@raw.data <- Allcells.data@raw.data[genes.use, ]

# Normalization and variable gene selection
Allcells.data <- NormalizeData(object = Allcells.data,
                         normalization.method = "LogNormalize", 
                         scale.factor = round(median(Allcells.data@meta.data$nUMI)),
                         display.progress = F)

Allcells.data <- FindVariableGenes(object = Allcells.data,
                             mean.function = ExpMean,
                             dispersion.function = LogVMR,
                             x.low.cutoff = 0.03,
                             x.high.cutoff = 4,
                             y.cutoff = 1.3, do.plot = F,
                             display.progress = F)
length(Allcells.data@var.genes)

## [1] 926

Initialize a monocle object

# Transfert metadata
meta.data <- data.frame(barcode = rownames(Allcells.data@meta.data),
                        Cluster = Allcells.data@meta.data$Cluster.ident,
                        Lineage = Allcells.data@meta.data$Lineage,
                        Pseudotime.Score = Allcells.data@meta.data$Pseudotime,
                        row.names = rownames(Allcells.data@meta.data))

Annot.data  <- new('AnnotatedDataFrame', data = meta.data)

# Transfert count data
count.data = data.frame(gene_short_name = rownames(Allcells.data@raw.data),
                        row.names = rownames(Allcells.data@raw.data))

feature.data <- new('AnnotatedDataFrame', data = count.data)

# Create the CellDataSet object
gbm_cds <- newCellDataSet(as.matrix(Allcells.data@raw.data),
                          phenoData = Annot.data,
                          featureData = feature.data,
                          lowerDetectionLimit = 1,
                          expressionFamily = negbinomial())

gbm_cds <- estimateSizeFactors(gbm_cds)
gbm_cds <- estimateDispersions(gbm_cds)
gbm_cds <- detectGenes(gbm_cds, min_expr = 0.1)

rm(list = ls()[!ls() %in% c("Allcells.data", "gbm_cds")])

Test each gene trend over pseudotime score

# Exclude cell cycle associated genes
CCgenes <- as.character(read.table("./Progenitors/CellCycleGenes.csv", sep = "\t", header = F)[,1])
Input.genes <- Allcells.data@var.genes[!Allcells.data@var.genes %in% CCgenes]

Speudotime.lineages.diff <- differentialGeneTest(gbm_cds[Input.genes,], 
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime.Score, df = 3)*Lineage", 
                                                 reducedModelFormulaStr = "~sm.ns(Pseudotime.Score, df = 3)", 
                                                 cores = detectCores() - 2)

#Filter based on FDR
Speudotime.lineages.diff.filtered <- Speudotime.lineages.diff %>% filter(qval < 5e-2)

Find direction of the DEG

We find direction of the DEG by calculating the area between curves (ABC) for branch-dependent genes by adapting the monocle package function calABCs. Genes specific ABC is computed on smoothed expression value over 100 points along the pseudotime

Smooth gene expression along pseudotime

# Create a new pseudo-DV vector of 500 points
nPoints <- 100

new_data = list()
for (Lineage in unique(pData(gbm_cds)$Lineage)){
  new_data[[length(new_data) + 1]] = data.frame(Pseudotime.Score = seq(min(pData(gbm_cds)$Pseudotime.Score), max(pData(gbm_cds)$Pseudotime.Score), length.out = nPoints), Lineage=Lineage)
}

new_data = do.call(rbind, new_data)

# Smooth gene expression
curve_matrix <- genSmoothCurves(gbm_cds[as.character(Speudotime.lineages.diff.filtered$gene_short_name),],
                                trend_formula = "~sm.ns(Pseudotime.Score, df = 3)*Lineage",
                                relative_expr = TRUE,
                                new_data = new_data,
                                cores= detectCores() - 2)

Compute the ABC for each genes

# Extract matrix containing Smooth curves for each lineages
Nr4a2_curve_matrix <- curve_matrix[, 1:nPoints] # the first 100 points correspond to Nr4a2 cells
Nfix_curve_matrix <- curve_matrix[, (nPoints + 1):(2 * nPoints)]
  
ABCs_res <- Nr4a2_curve_matrix - Nfix_curve_matrix # Direction of the comparison : postive ABCs <=> Upregulated in Nr4a2 lineage
ILR_res <- log2(Nr4a2_curve_matrix/ (Nfix_curve_matrix + 0.1)) # Average logFC between the 2 curves
  
ABCs_res <- apply(ABCs_res, 1, function(x, nPoints) {
                  avg_delta_x <- (x[1:(nPoints - 1)] + x[2:(nPoints)])/2
                  step <- (100/(nPoints - 1))
                  res <- round(sum(avg_delta_x * step), 3)
                  return(res)},
                  nPoints = nPoints) # Compute the area below the curve
  
ABCs_res <- cbind(ABCs_res, ILR_res[,ncol(ILR_res)])
colnames(ABCs_res)<- c("ABCs", "Endpoint_ILR")

# Import ABC values into the DE test results table
Speudotime.lineages.diff.filtered <- cbind(Speudotime.lineages.diff.filtered[,1:4],
                                           ABCs_res,
                                           Speudotime.lineages.diff.filtered[,5:6])

Cluster genes by expression profiles along the two trajectories

Nr4a2 neurons specific transcriptional trajectories

# Extract Nr4a2 neurons trajectory genes
Nr4a2.res <- as.data.frame(Speudotime.lineages.diff.filtered[Speudotime.lineages.diff.filtered$ABCs > 0,])
Nr4a2.genes <- row.names(Nr4a2.res)
Nr4a2_curve_matrix <- Nr4a2_curve_matrix[rownames(Nr4a2_curve_matrix) %in% Nr4a2.genes, ]

# Groupe genes in 6 clusters by partitioning round medoids
Nr4a2.genes.clusters <- cluster::pam(as.dist((1 - cor(t(Nr4a2_curve_matrix), method = "pearson"))), k=6)

# Create a dataframe storing DEG test and clustering results
Nr4a2.Gene.dynamique <- data.frame(Gene= names(Nr4a2.genes.clusters$clustering),
                                   pval=Nr4a2.res$pval,
                                   qval=Nr4a2.res$qval,
                                   ABCs=Nr4a2.res$ABCs,
                                   Gene.Clusters= paste0("Clust.", Nr4a2.genes.clusters$clustering)) %>% arrange(Gene.Clusters)

row.names(Nr4a2.Gene.dynamique) <- Nr4a2.Gene.dynamique$Gene

write.table(Nr4a2.Gene.dynamique, "./Nr4a2.Gene.dynamique.csv", sep=";")

# Order the row using seriation
dst <- as.dist((1-cor(scale(t(Nr4a2_curve_matrix)), method = "pearson")))
row.ser <- seriate(dst, method ="R2E") #"R2E"
gene.order <- rownames(Nr4a2_curve_matrix[get_order(row.ser),])

anno.colors <- list(lineage = c(Nfix="#cc391b", Nr4a2="#026c9a"),
                    Gene.Clusters = c(Clust.1 ="#b79f0b" , Clust.2="#e46B6b", Clust.3="#e7823a", Clust.4="#cc8778", Clust.5="#68b041", Clust.6="#5ab793"))


pheatmap::pheatmap(curve_matrix[gene.order,
                                c(200:101, #Nfix points
                                  1:100)], #Nr4a2 points
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = Nr4a2.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Nr4a2", "Nfix"), each=100)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = T,
                   fontsize_row = 2,
                   color = rev(brewer.pal(11,"RdBu")),
                   breaks = seq(-2.5,2.5, length.out = 11),
                   main = "Nr4a2 cells enriched genes expression along pseudotime")

Manuscript Fig. 7D

Nfix neurons specific transcriptional trajectories

# Extract Nfix neurons trajectory genes
Nfix.res <- as.data.frame(Speudotime.lineages.diff.filtered[Speudotime.lineages.diff.filtered$ABCs < 0,])
Nfix.genes <- row.names(Nfix.res)
Nfix_curve_matrix <- Nfix_curve_matrix[rownames(Nfix_curve_matrix) %in% Nfix.genes, ]

# Groupe genes in 6 clusters by partitioning round medoids
Nfix.genes.clusters <- cluster::pam(as.dist((1 - cor(t(Nfix_curve_matrix), method = "pearson"))), k=6)

# Create a dataframe storing DEG test and clustering results
Nfix.Gene.dynamique <- data.frame(Gene= names(Nfix.genes.clusters$clustering),
                                   pval=Nfix.res$pval,
                                   qval=Nfix.res$qval,
                                   ABCs=Nfix.res$ABCs,
                                   Gene.Clusters= paste0("Clust.", Nfix.genes.clusters$clustering)) %>% arrange(Gene.Clusters)

row.names(Nfix.Gene.dynamique) <- Nfix.Gene.dynamique$Gene

write.table(Nfix.Gene.dynamique, "./Nfix.Gene.dynamique.csv", sep=";")

# Order the row using seriation
dst <- as.dist((1-cor(scale(t(Nfix_curve_matrix)), method = "pearson")))
row.ser <- seriate(dst, method ="R2E")
gene.order <- rownames(Nfix_curve_matrix[get_order(row.ser),])


pheatmap::pheatmap(curve_matrix[gene.order,
                                c(200:101, #Nfix points
                                  1:100)], #Nr4a2 points
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = Nfix.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = data.frame(lineage = rep(c("Nr4a2", "Nfix"), each=100)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = T,
                   fontsize_row = 2,
                   color = rev(brewer.pal(11,"RdBu")),
                   breaks = seq(-2.5,2.5, length.out = 11),
                   main = "Nfix cells enriched genes expression along pseudotime")

Manuscript Fig. 7C

Plot relevant genes trend

# load custom plotting functions
source("./functions/TrajectoriesPlotFunctions.R")

Plot.Genes.trend(Allcells.data,
                 genes = c("Nfib", "Dbx1",
                           "Neurod6", "Pbx3",
                           "Pcp4", "Zfhx3"),
                 color.by = "lineage",
                 Use.scale.data = F)

Manuscript Fig. 7E

Plot.Genes.trend(Allcells.data,
                 genes = c("Neurod4", "Neurog2",
                           "Zfhx3", "Tubb3",
                           "Hey1", "Mapt"),
                 color.by = "lineage",
                 Use.scale.data = F)

Manuscript Fig. 7E

Plot.Genes.trend(Allcells.data,
                 genes = c("Tbr1", "Emx1",
                           "Neurod1", "Hey1",
                           "Neurod2", "Flrt3"),
                 color.by = "lineage",
                 Use.scale.data = F)

Manuscript Fig. S7B

Plot.Genes.trend(Allcells.data,
                 genes = c("Nfia", "Dmrt3",
                           "Cnr1", "Slc30a10"),
                 color.by = "lineage",
                 Use.scale.data = F)

Manuscript Fig. S7B

Plot.Genes.trend(Allcells.data,
                 genes = c("Meis2", "Gm29260",
                           "Nr2f2", "Dleu7",
                           "Zbtb20", "Svil"),
                 color.by = "lineage",
                 Use.scale.data = F)

Manuscript Fig. S7B

Plot.Genes.trend(Allcells.data,
                 genes = c("Epha3", "Pdlim4",
                           "Tox", "Tfap2c"),
                 color.by = "lineage",
                 Use.scale.data = F)

Manuscript Fig. S7B

Plot.Genes.trend(Allcells.data,
                 genes = c("Neurog1", "Eomes",
                           "Btg2", "Foxg1",
                           "Emx2"),
                 color.by = "lineage",
                 Use.scale.data = F)

Manuscript Fig. S7B

Inspect genes which best predict early fate bias within BPs

Extract cells in BP state

set.seed(100)
# Run Kmeans clustering
cl <- kmeans(cbind(Allcells.data@meta.data$AP_signature1,
                   Allcells.data@meta.data$BP_signature1,
                   Allcells.data@meta.data$EN_signature1,
                   Allcells.data@meta.data$LN_signature1), 4)

Allcells.data@meta.data$kmeanClust <- paste0("Clust.",cl$cluster)

col.pal <- wes_palette("GrandBudapest1", 4, type = "discrete")

p1 <- ggplot(Allcells.data@meta.data, aes(x=AP_signature1, y=BP_signature1, colour = kmeanClust)) +
  scale_color_manual(values=col.pal) +
  geom_point() + 
  theme(legend.position="none")
ggMarginal(p1, type = "histogram", fill="lightgrey")

DimPlot(Allcells.data,
        group.by = "kmeanClust",
        reduction.use = "spring",
        cols.use = col.pal,
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = F)

# Find the kmeans cluster with the highest mean Pal_signature1 score
MeanKclust.BPscore <- aggregate(BP_signature1 ~ kmeanClust, Allcells.data@meta.data, mean)
BPclust <- MeanKclust.BPscore %>% filter(BP_signature1 == max(BP_signature1)) %>% pull(kmeanClust)

# Extract barcodes and filter the seurat object
Glut.cells <- Allcells.data@meta.data %>% filter(kmeanClust == BPclust) %>% pull(Barcodes)
BP.data <-  SubsetData(Allcells.data, cells.use = Glut.cells , subset.raw = T,  do.clean = F)

DimPlot(BP.data,
        group.by = "Lineage",
        reduction.use = "spring",
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        cols.use = c("#cc391b","#026c9a"),
        label.size = 4,
        no.legend = F)

#Filter genes
num.cells <- Matrix::rowSums(BP.data@raw.data > 0)
genes.use <- names(x = num.cells[num.cells >= 10])
BP.data@raw.data <- BP.data@raw.data[genes.use, ]

# Normalization and variable gene selection
BP.data <- NormalizeData(object = BP.data,
                         normalization.method = "LogNormalize", 
                         scale.factor = round(median(BP.data@meta.data$nUMI)),
                         display.progress = F)

BP.data <- FindVariableGenes(object = BP.data,
                             mean.function = ExpMean,
                             dispersion.function = LogVMR,
                             x.low.cutoff = 0.03,
                             x.high.cutoff = 4,
                             y.cutoff = 1.3, do.plot = F,
                             display.progress = F)

Find genes which best classify BP between the 2 trajectories

# Exclude cell cycle associated, mt and ribo ribo
CCgenes <- as.character(read.table("/home/matthieu/Bureau/R/FiguresCodes/Progenitors/CellCycleGenes.csv", sep = "\t", header = F)[,1])
GenesToRemove <- c(grep(pattern = "(^Rpl|^Rps|^Mrp)", x = row.names(BP.data@data), value = TRUE),
                   grep(pattern = "^mt-", x = row.names(BP.data@data), value = TRUE),
                   "Xist", CCgenes)

Input.genes <- BP.data@var.genes[!BP.data@var.genes %in% GenesToRemove]

# Set ident
BP.data <- SetAllIdent(BP.data, id = "Lineage")

# We select all genes which are differentialy expressed between Nfix and Nr4a2 biased BP as input to smoothing 
NfixvsNr4a2.DEgenes <- FindMarkers(object = BP.data,
                                   test.use = "roc",
                                   ident.1 = "Nfix.lineage",
                                   ident.2 = "Nr4a2.lineage",
                                   min.pct = 0,
                                   logfc.threshold = 0,
                                   print.bar = F,
                                   only.pos = F,
                                   genes.use = Input.genes)

NfixvsNr4a2.DEgenes$Gene <- rownames(NfixvsNr4a2.DEgenes)

#Set a new data frame which will store all variable needed to generate the volcano plot
Vplot.data <- data.frame(gene = NfixvsNr4a2.DEgenes$Gene,
                         AUC = NfixvsNr4a2.DEgenes$myAUC, 
                         LogFC = NfixvsNr4a2.DEgenes$avg_logFC)

temporal.module <- row.names(read.table("./Progenitors/Temporal.gene.clusters.csv", sep = ";", header = T, row.names = 1))
spatial.module <- row.names(read.table("./Progenitors/Spatial.gene.clusters.csv", sep = ";", header = T, row.names = 1))

Vplot.data <- dplyr::mutate(Vplot.data, color = dplyr::case_when(Vplot.data$gene %in% temporal.module ~ "Temporal",
                                                                 Vplot.data$gene %in% spatial.module ~ "Spatial",
                                                                 TRUE ~ "NA"))

ggplot(Vplot.data, aes(x = LogFC, y = AUC, color = color)) +
  geom_point(size = 2, alpha = 0.8, na.rm = T) +
  scale_color_manual(name = "AP module",
                     values = c("Temporal" = "#cc391b",
                                "Spatial" = "#026c9a",
                                "NA" = "#969696")) +
  ylim(0,NA) +
  xlab(expression(log("Fold change"))) +
  ylab("AUC") +
  geom_vline(xintercept = c(-0.4, 0.4), colour = "#969696") +
  geom_hline(yintercept = c(0.65, 0.35), colour = "#969696") +
  ggrepel::geom_text_repel(aes(label=ifelse(gene %in% c(temporal.module,spatial.module) & abs(Vplot.data$LogFC) > 0.4 & (Vplot.data$AUC > 0.65 | Vplot.data$AUC < 0.35), as.character(gene),'')),
                  box.padding   = 0.35, 
                  point.padding = 0.5,
                  segment.color = 'grey50',
                  colour = "black")

Plot gene on Spring plot

FeaturePlot(object = BP.data,
            features.plot = c("Emx1", "Zbtb20"),
            cols.use = c("grey", "red"),
            pt.size = 2,
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F)

Session Info

#date
format(Sys.time(), "%d %B, %Y, %H,%M")

## [1] "20 novembre, 2020, 17,26"

#Packages used
sessionInfo()

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] splines   stats4    parallel  stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] wesanderson_0.3.6   RColorBrewer_1.1-2  patchwork_0.0.1    
##  [4] ggExtra_0.9         reshape2_1.4.3      dplyr_0.8.3        
##  [7] seriation_1.2-9     velocyto.R_0.6      FateID_0.1.9       
## [10] monocle_2.14.0      DDRTree_0.1.5       irlba_2.3.3        
## [13] VGAM_1.1-2          Biobase_2.46.0      BiocGenerics_0.32.0
## [16] princurve_2.1.4     Seurat_2.3.4        Matrix_1.2-17      
## [19] cowplot_1.0.0       ggplot2_3.2.1      
## 
## loaded via a namespace (and not attached):
##   [1] snow_0.4-3           backports_1.1.5      Hmisc_4.3-0         
##   [4] plyr_1.8.4           igraph_1.2.5         lazyeval_0.2.2      
##   [7] densityClust_0.3     lle_1.1              fastICA_1.2-2       
##  [10] digest_0.6.25        foreach_1.4.7        htmltools_0.5.0     
##  [13] viridis_0.5.1        lars_1.2             gdata_2.18.0        
##  [16] magrittr_1.5         checkmate_1.9.4      cluster_2.1.0       
##  [19] mixtools_1.1.0       ROCR_1.0-7           limma_3.42.0        
##  [22] matrixStats_0.55.0   R.utils_2.9.0        docopt_0.6.1        
##  [25] askpass_1.1          colorspace_1.4-1     ggrepel_0.8.1       
##  [28] xfun_0.18            sparsesvd_0.2        crayon_1.3.4        
##  [31] jsonlite_1.7.0       zeallot_0.1.0        survival_2.44-1.1   
##  [34] zoo_1.8-6            iterators_1.0.12     ape_5.3             
##  [37] glue_1.4.1           registry_0.5-1       gtable_0.3.0        
##  [40] kernlab_0.9-29       prabclus_2.3-1       DEoptimR_1.0-8      
##  [43] scales_1.1.0         pheatmap_1.0.12      som_0.3-5.1         
##  [46] bibtex_0.4.2         miniUI_0.1.1.1       Rcpp_1.0.5          
##  [49] metap_1.1            dtw_1.21-3           xtable_1.8-4        
##  [52] viridisLite_0.3.0    htmlTable_1.13.2     reticulate_1.13     
##  [55] foreign_0.8-72       bit_4.0.4            proxy_0.4-23        
##  [58] mclust_5.4.5         SDMTools_1.1-221.1   Formula_1.2-3       
##  [61] tsne_0.1-3           umap_0.2.3.1         htmlwidgets_1.5.1   
##  [64] httr_1.4.1           FNN_1.1.3            gplots_3.0.1.1      
##  [67] fpc_2.2-3            acepack_1.4.1        modeltools_0.2-22   
##  [70] ica_1.0-2            farver_2.0.1         pkgconfig_2.0.3     
##  [73] R.methodsS3_1.7.1    flexmix_2.3-15       nnet_7.3-14         
##  [76] locfit_1.5-9.1       labeling_0.3         later_1.0.0         
##  [79] tidyselect_0.2.5     rlang_0.4.7          munsell_0.5.0       
##  [82] tools_3.6.3          ggridges_0.5.1       fastmap_1.0.1       
##  [85] evaluate_0.14        stringr_1.4.0        yaml_2.2.1          
##  [88] npsurv_0.4-0         knitr_1.26           bit64_4.0.2         
##  [91] fitdistrplus_1.0-14  robustbase_0.93-5    caTools_1.17.1.2    
##  [94] randomForest_4.6-14  purrr_0.3.3          RANN_2.6.1          
##  [97] pbapply_1.4-2        nlme_3.1-141         mime_0.7            
## [100] slam_0.1-46          R.oo_1.23.0          hdf5r_1.3.2.9000    
## [103] compiler_3.6.3       rstudioapi_0.11      png_0.1-7           
## [106] lsei_1.2-0           tibble_2.1.3         stringi_1.4.6       
## [109] highr_0.8            lattice_0.20-41      HSMMSingleCell_1.6.0
## [112] vctrs_0.2.0          pillar_1.4.2         lifecycle_0.1.0     
## [115] combinat_0.0-8       Rdpack_0.11-0        lmtest_0.9-37       
## [118] data.table_1.12.6    bitops_1.0-6         gbRd_0.4-11         
## [121] httpuv_1.5.2         pcaMethods_1.78.0    R6_2.4.1            
## [124] latticeExtra_0.6-28  promises_1.1.0       TSP_1.1-10          
## [127] KernSmooth_2.23-15   gridExtra_2.3        codetools_0.2-16    
## [130] MASS_7.3-53          gtools_3.8.1         assertthat_0.2.1    
## [133] openssl_1.4.1        withr_2.1.2          qlcMatrix_0.9.7     
## [136] mgcv_1.8-33          diptest_0.75-7       doSNOW_1.0.18       
## [139] grid_3.6.3           rpart_4.1-15         tidyr_1.0.0         
## [142] class_7.3-17         rmarkdown_2.5        segmented_1.0-0     
## [145] Rtsne_0.15           shiny_1.4.0          snowfall_1.84-6.1   
## [148] scatterplot3d_0.3-41 base64enc_0.1-3

---

1. Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France↩